这篇文章主要讲解了python实现分组求和与分组累加求和的方法，内容清晰明了，对此有兴趣的小伙伴可以学习一下，相信大家阅读完之后会有帮助。

我就废话不多说了，大家还是直接看代码吧！

# -*- encoding=utf-8 -*-import pandas as pddata=['abc','abc','abc','asc','ase','ase','ase']num=[1,2,2,1,2,1,2]df1=pd.DataFrame({'name':data,'num':num})print(df1)df1['mmm']=df1['num']df2=df1.groupby(['name', 'num'], as_index=False).count()print(df2)df2.sort_values(['name', 'num'], ascending=[1, 1], inplace=True)print(df2)df2['sum']=df2.groupby(['name'])['mmm'].cumsum()print(df2)kk=df2.groupby(['name'],as_index=False)['num'].sum()print(kk)df3 = pd.merge(df2, kk, on='name', how='left',)print(df3)df3['ratio']=df3['sum']/df3['num_y']df3.columns = ['name', 'num', 'mmm', 'sum','numsum','ratio']print(df3)df4=df3.groupby(['mmm'],as_index=False)['ratio'].mean()print(df4)

运行：

name num0 abc 11 abc 22 abc 23 asc 14 ase 25 ase 16 ase 2 name num mmm0 abc 1 11 abc 2 22 asc 1 13 ase 1 14 ase 2 2 name num mmm0 abc 1 11 abc 2 22 asc 1 13 ase 1 14 ase 2 2 name num mmm sum0 abc 1 1 11 abc 2 2 32 asc 1 1 13 ase 1 1 14 ase 2 2 3 name num0 abc 31 asc 12 ase 3 name num_x mmm sum num_y0 abc 1 1 1 31 abc 2 2 3 32 asc 1 1 1 13 ase 1 1 1 34 ase 2 2 3 3 name num mmm sum numsum ratio0 abc 1 1 1 3 0.3333331 abc 2 2 3 3 1.0000002 asc 1 1 1 1 1.0000003 ase 1 1 1 3 0.3333334 ase 2 2 3 3 1.000000 mmm ratio0 1 0.5555561 2 1.000000Process finished with exit code 0

补充知识：python项目篇-对符合条件的某个字段进行求和,聚合函数annotate(),aggregate()函数

对符合条件的某个字段求和

需求是，计算每日的收入和

1、

new_dayincome = request.POST.get("dayincome_time", None) # total_income = models.bathAccount.objects.filter(dayBath=new_dayincome).aggregate(nums=Sum('priceBath')) total_income = models.bathAccount.objects.values('priceBath').annotate(nums=Sum('priceBath')).filter(dayBath=new_dayincome) print("total_income",total_income[0]['nums'])

输出结果：total_income 132

2、

from django.db.models import Sum,Countnew_dayincome = request.POST.get("dayincome_time", None) total_income = models.bathAccount.objects.filter(dayBath=new_dayincome).aggregate(nums=Sum('priceBath')) print("total_income",total_income['nums'])

输出结果:total_income 572

第二种输出的是正确的数字

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